In recent years, deep learning has made major breakthroughs in the field of artificial intelligence, and, more particularly, has achieved breakthrough progress and desired effects in fields such as image, voice, and natural language processing. Deep learning may make full use of powerful computing capability, construct a complex non-linear model by means of multi-layer neural network cascading, and solve end-to-end problems directly based on massive data.
When solving problems by using a Deep Neural Network (DNN), people usually tend to design a more complex network to collect more data in expectation of higher performance. A current DNN has a relatively large number of layers and a relatively large number of connection weights, and the connection weight is usually a 32-bit single-precision floating-point number or a 64-bit double-precision floating-point number. The connection weight is a value used to measure the strength of a connection between an upper-layer neuron and a lower-layer neuron in the neural network. During DNN computing, a floating-point multiplier needs to be invoked to perform a large number of floating-point number multiplication operations, resulting in a relatively low DNN computing speed.